How important are out-of-pocket costs to rural patients' cancer care decisions?
Bibliographic record
Abstract
OBJECTIVE: We examined the importance of 5 items (stage of illness, personal feelings, travel costs, drug costs and child care costs) in the cancer treatment decisions of urban and rural residents after they had started treatment for their cancer. METHODS: We surveyed 484 adults who presented for care at cancer clinics in Newfoundland and Labrador from September 2002 to June 2003. Respondents rated the importance of each of the 5 items in their cancer care decisions on a 5-point Likert scale, which was later collapsed into 2 categories, "important" and "not important." We used chi2 tests and multiple logistic regression to compare the responses of urban and rural residents. RESULTS: In our sample of 484 respondents, there were 258 (53.3%) urban and 226 (46.7%) rural residents. After controlling for other significant predictors, we found that rural residents were more likely to report that travel costs (odds ratio [OR] 1.79, 95% confidence interval [CI] 1.21-2.63), drug costs (OR 1.69, 95% CI 1.13-2.23) and child care costs (OR 2.33, 95% CI 1.09-4.96) were "important" in cancer treatment decisions compared with urban residents. Stage of disease and personal feelings were equally important to urban and rural residents. CONCLUSION: Financial impediments disproportionately affect rural residents' decisions about cancer care and highlight the need to ensure that centralized specialist care, such as cancer treatment, is accessible.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".